US7519206B2 - Detection of features in images - Google Patents

Detection of features in images Download PDF

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Publication number
US7519206B2
US7519206B2 US10/432,376 US43237603A US7519206B2 US 7519206 B2 US7519206 B2 US 7519206B2 US 43237603 A US43237603 A US 43237603A US 7519206 B2 US7519206 B2 US 7519206B2
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image
feature
features
profile
intensity
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US20040047498A1 (en
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Miguel Mulet-Parada
Julia Alison Noble
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Siemens Medical Solutions USA Inc
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Siemens Medical Solutions USA Inc
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/168—Segmentation; Edge detection involving transform domain methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac

Definitions

  • the present invention relates to a method and apparatus for processing images to detect particular features in the image. It is particularly concerned with the detection of features in noisy 2D, 2D+T, 3D and 3D+T images, and is thus particularly useful in medical imaging, for instance using ultrasound.
  • the present invention provides a method for detecting features of interest in an image based on the shape of the intensity profile of the feature, rather than its intensity. This is achieved by an intensity independent comparison of the intensity profile across the image with a shape model of features of interest. To improve subsequent processing of the image, areas of the image which are detected as corresponding to the shape model are labelled with a description of their properties.
  • the present invention provides a method of detecting features-of-interest in an image, comprising the steps of:
  • the label may include a measure of the orientation of the feature in the image, and the comparison may also be performed on an intensity profile taken across successive frames of an image sequence to derive a measure of the velocity of image features.
  • the label may then comprise a measure of the velocity.
  • the comparison with the shape model is advantageously performed in the spatial or spatio temporal frequency domains, thus by decomposing the intensity profile into spatial frequency components and examining the phase and amplitude of those components.
  • this is achieved by convolving the intensity profile with a quadrature filter to derive the phase and amplitude of a pair of odd and even components of the profile in the spatial frequency domain.
  • the phase and amplitude of these components is characteristic of different shapes in the intensity profile.
  • the difference between the odd and even components which is a measure of the “feature asymmetry” is, for example, a maximum for a step edge.
  • the values of feature asymmetry and local amplitude (which is based on the amplitude of the two components), are included in the label.
  • the filters are quadrature wavelet filters, e.g. log-Gabor filters, so that the intensity profile is convolved with odd and even wavelets.
  • the scale of the wavelet is chosen in accordance with the scale of the features to be detected, and to be much larger than the scale of noise in the image. This means that the technique selects features according to their shape and scale, but regardless of the value of intensity.
  • the filters may be oriented in different directions across the image to detect image features having different orientations.
  • the label may then include a measure of the feature orientation, which can be derived from the relative responses of the differently oriented filters.
  • Such search criteria may include a condition on the value of the feature asymmetry of the two features being detected and a condition on the orientation of the detected features (for instance that they are oriented similarly).
  • the search area from frame-to-frame may be defined in accordance with the velocity, and the search criteria may include a condition on the velocity.
  • the invention is particularly applicable to ultrasound or magnetic resonance images, but also to x-ray and other imaging modalities.
  • the embodiment described below relates to echocardiography, but the invention is applicable to ultrasound images in general, such as of different organs and tissues e.g. coronary arteries, liver, foetus etc, and to different ultrasound modalities such as the use of contrast and different signal processing techniques such as Doppler imaging and harmonic imaging.
  • the invention is adaptable to the detection of any features corresponding to a step change in intensity, such as the ventricular walls.
  • the invention may be embodied in a system adapted to perform the image processing method, and it may be embodied by a computer program comprising program code means for executing the method.
  • the invention further provides for a computer program storage medium carrying such a computer program, and also a computer system programmed to carry out the method.
  • FIGS. 1( a ) and ( b ) illustrate Fourier decompositions of a step edge and a triangular function
  • FIG. 2 illustrates the values of the local phase signatures of spatial frequency components for different functions
  • FIGS. 3( a ) and ( b ) illustrate respectively even and odd two-octave log-Gabor wavelet functions
  • FIG. 4 illustrates the spectral properties of the log-Gabor wavelets
  • FIG. 5( a ) illustrates a typical intensity profile across an ultrasound image
  • FIG. 5( b ) illustrates the local amplitude result of a convolution of an intensity profile with log-Gabor filters at a succession of scales
  • FIG. 5( c ) illustrates the local phase result of a convolution of an intensity profile with log-Gabor filters at a succession of scales
  • FIG. 6 schematically illustrates an image processing system according to an embodiment of the invention
  • FIGS. 7 (A) and (B) illustrate examples of the feature orientations in two and three dimensions used in the embodiment of FIG. 6 ;
  • FIG. 8 illustrates the effect on feature detection of analysing the intensity profile using log-Gabor wavelets of different scale (wavelengths);
  • FIG. 9 illustrates a process for tracking a detected feature through a sequence of images
  • FIG. 10 schematically illustrates part of the tracking process
  • FIG. 11 illustrates the relationship between local phase and feature asymmetry.
  • FIG. 1 illustrates Fourier decompositions of a step edge ( FIG. 1( a )) and a triangular function ( FIG. 1( b )).
  • FIG. 1( a ) it can be seen that three sinusoidal components i, ii and iii sum to make an approximation to a step edge iv.
  • phase of all three components is the same, zero, at the positive-going step edge. Further the phase value of all the components at the negative step edge is 180 degrees. Reference to FIG. 1( b ) illustrates that for a triangular peak the phase value of all of the Fourier components is 90 degrees at the peak of the triangle.
  • the “local phase” is defined as the phase offset of each Fourier component describing a signal, measured at the event of interest. For features such as a step, the peak of a roof or the foot of a ramp, the local phase of all of the Fourier components converges to the same value. Further, the value is characteristic of the type of feature.
  • the phase values for different types of feature is illustrated in the diagram of FIG. 2 . It can be seen, therefore, that the phase of the spectral components of a signal gives an indication of the shape of the signal. It should be noted that the particular value of phase is arbitrary (depending on the position of the origin), what is important is that the different features have different, known values.
  • this embodiment of the invention looks in turn at successive small sections of the intensity profile across the image by making the spectral analysis using wavelets.
  • This embodiment uses log-Gabor wavelets as illustrated in FIG. 3 (which are in fact Gaussian modulated sinusoids) which have a non-zero central section and are zero elsewhere. Thus convolving them with the intensity profile effectively windows a small region of the profile.
  • FIG. 3( a ) shows the even wavelet based on a Gaussian modulated cosine
  • FIG. 3( b ) the odd wavelet based on a Gaussian modulated sine function.
  • the spectral properties (plotted against frequency and log-frequency) of the wavelets are shown in FIG. 4 .
  • FIG. 5( a ) illustrates a typical intensity profile across an echocardiographic image.
  • FIGS. 3( a ) and 3 ( b ) illustrate very small scale versions of the wavelets shown in FIGS. 3( a ) and 3 ( b ). This would result in an analysis of the very small scale variations in intensity in the profile.
  • the scale of the wavelets used was much larger, such that, for example, only one wavelet fitted across the entire length of the profile, the effect of the small scale variations in intensity would not affect the result, and the analysis would only actually pick up the broad overall shape of the profile.
  • the effect of varying the scale of the wavelet is to look for different scale features in the profile.
  • FIGS. 5( b ) and ( c ) The effect of varying the wavelength, or scale, of the wavelet is illustrated by the scalograms of FIGS. 5( b ) and ( c ). These scalograms are constructed by taking the convolutions of a sequence of log-Gabor filters of increasing wavelength, and stacking vertically the output local phase or local amplitude responses of each filter, so that each forms a new row of the image. The scalograms on the left ( FIG.
  • ⁇ ⁇ ( x ) ⁇ arctan ⁇ ( e ⁇ ( x ) o ⁇ ( x ) ) ⁇
  • the intensity values correspond to the output amplitudes of the convolutions.
  • the phase values on the right have been mapped to intensities so that black corresponds to zero phase (positive steps) and white corresponds to ⁇ radians (negative steps). Peaks, having ⁇ /2 phase signatures, are grey.
  • phase scalogram shows that at small scales many features corresponding to these extreme values are found. However, as the filter scale increases, the number of features reduces to a few straight bands. Among these it is possible to identify the step edges (local phase converges to zero, thus black on the scalogram) corresponding to the epicardial ( 1 ) and the endocardial ( 2 , 3 ) step edges, as well as a pericardial peak ( 4 ). From the phase scalogram such as FIG. 5 c, two observations can be made: 1) features such as step edges and peaks have stable phase values and 2) phase signatures remain localised over scales.
  • Stability means that there is always a particular phase signature associated with each feature shape over scales (in the illustration the zero degree and ⁇ radian values corresponding to positive and negative steps respectively are shown).
  • Localisation means that the local phase signature associated with a feature remains in the proximity of the event (i.e. at the same place on the x-axis) over a range of scales.
  • the system can use a large scale filter to remove noise and still identify feature points accurately, and by examining a phase scalogram such as FIG. 5( c ) it is possible to set a suitable scale at which the phase signatures of features of interest are clear.
  • the system has provided a particularly rich description of the properties of the detected features.
  • This rich description can be used to advantage in further processing of the image, particularly when compared with techniques which just mark a pixel as belonging to a detected feature or not. An example of such further processing will be described below, with reference to FIGS. 9 and 10 .
  • the feature being detected is the left ventricular wall (endocardium) which is a closed contour having a shape as illustrated at 20 in FIG. 9 .
  • endocardium an open contour having a shape as illustrated at 20 in FIG. 9 .
  • the contour detected in a sequence of images of the beating heart should follow the movement of the endocardium. While ideally it would be possible simply to detect the image features corresponding to the endocardium in each frame of the sequence, in practice noise and artifacts tend to produce a disconnected representation of the cardiac boundaries, and a number of spurious responses unrelated to the cardiac walls. Tracking the feature through a sequence of images can interpolate the features detected in the individual frames to produce a continuous contour which follows, or tracks, the feature in the image.
  • one of the three pixels was selected as a possible candidate if it has the strongest local amplitude response amongst the three and has a feature asymmetry score greater than 0.8 and if the difference in orientation between the contour normal and the feature normal is less than 5 degrees.
  • the candidates are selected making use of the labels attached during the feature detection process.
  • the orientation measure takes into consideration the direction of the step edge (whether it is a positive or negative step).
  • the velocity estimate for the points can be used to restrict the search space along the normal.
  • the length of the search slab is selected according to the velocity of the feature.
  • the cost function is designed to blend the local amplitude values and orientation information and it includes partial costs such as:—a large penalty cost if an entry on the normal 24 has been left blank because no feature has been found in the image; the cost related to the local amplitude measurement, the distance away from the previous contour point and a smoothing term.
  • the optimal path is the one that links one point from each normal such that the sum of all costs is minimal.
  • a least squares transformation is then used to fit the contour inherited from the adjacent frame to the points selected by the dynamic programming routine.

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GBGB0028491.9A GB0028491D0 (en) 2000-11-22 2000-11-22 Detection of features in images
PCT/GB2001/005094 WO2002043004A2 (fr) 2000-11-22 2001-11-19 Detection de caracteristiques dans des images

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